




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow

@require_torch
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				Optional[Any]  =     '''ylacombe/bark-small'''
         UpperCAmelCase_     :				int  =     tempfile.mkdtemp()
         UpperCAmelCase_     :				Union[str, Any]  =     '''en_speaker_1'''
         UpperCAmelCase_     :				List[str]  =     '''This is a test string'''
         UpperCAmelCase_     :				Any  =     '''speaker_embeddings_path.json'''
         UpperCAmelCase_     :				List[str]  =     '''speaker_embeddings'''





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						List[str]:
         return AutoTokenizer.from_pretrained(self.checkpoint  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						List[str]:
         shutil.rmtree(self.tmpdirname    )





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				Tuple  =     self.get_tokenizer()

         UpperCAmelCase_     :				List[Any]  =     BarkProcessor(tokenizer=_SCREAMING_SNAKE_CASE    )

         processor.save_pretrained(self.tmpdirname    )
         UpperCAmelCase_     :				Optional[Any]  =     BarkProcessor.from_pretrained(self.tmpdirname    )

         self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer.get_vocab()    )





    @slow
    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				List[Any]  =     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint  ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,)
         processor.save_pretrained(
             self.tmpdirname  ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,speaker_embeddings_directory=self.speaker_embeddings_directory  ,)

         UpperCAmelCase_     :				Dict  =     self.get_tokenizer(bos_token='''(BOS)'''  ,eos_token='''(EOS)'''    )

         UpperCAmelCase_     :				List[Any]  =     BarkProcessor.from_pretrained(
             self.tmpdirname  ,self.speaker_embeddings_dict_path  ,bos_token='''(BOS)'''  ,eos_token='''(EOS)'''  ,)

         self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer_add_kwargs.get_vocab()    )





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				Optional[Any]  =     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint  ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,)

         UpperCAmelCase_     :				Tuple  =     35
         UpperCAmelCase_     :				List[str]  =     2
         UpperCAmelCase_     :				List[str]  =     8

         UpperCAmelCase_     :				List[Any]  =     {
             '''semantic_prompt''': np.ones(_SCREAMING_SNAKE_CASE    ),
             '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)    ),
             '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)    ),
         }

         # test providing already loaded voice_preset
         UpperCAmelCase_     :				Dict  =     processor(text=self.input_string  ,voice_preset=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[str]  =     inputs['''history_prompt''']
         for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()  ,processed_voice_preset.get(_SCREAMING_SNAKE_CASE  ,np.array([]    )    ).tolist()    )

         # test loading voice preset from npz file
         UpperCAmelCase_     :				Union[str, Any]  =     os.path.join(self.tmpdirname  ,'''file.npz'''    )
         np.savez(_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[Any]  =     processor(text=self.input_string  ,voice_preset=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[str]  =     inputs['''history_prompt''']

         for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()  ,processed_voice_preset.get(_SCREAMING_SNAKE_CASE  ,np.array([]    )    ).tolist()    )

         # test loading voice preset from the hub
         UpperCAmelCase_     :				Dict  =     processor(text=self.input_string  ,voice_preset=self.voice_preset    )





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				Any  =     self.get_tokenizer()

         UpperCAmelCase_     :				Any  =     BarkProcessor(tokenizer=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				str  =     processor(text=self.input_string    )

         UpperCAmelCase_     :				Any  =     tokenizer(
             self.input_string  ,padding='''max_length'''  ,max_length=256  ,add_special_tokens=_SCREAMING_SNAKE_CASE  ,return_attention_mask=_SCREAMING_SNAKE_CASE  ,return_token_type_ids=_SCREAMING_SNAKE_CASE  ,)

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]  ,encoded_processor[key].squeeze().tolist()    )

"""simple docstring"""



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = RobertaPreLayerNormConfig.from_pretrained(
        _A       ,   architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

    # convert state_dict
    _UpperCAmelCase        = torch.load(hf_hub_download(repo_id=_A       ,   filename="""pytorch_model.bin"""   )   )
    _UpperCAmelCase        = {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("""roberta."""   ):
            _UpperCAmelCase        = """roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
            continue

        _UpperCAmelCase        = tensor_value

    _UpperCAmelCase        = RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=_A       ,   config=_A       ,   state_dict=_A   )
    model.save_pretrained(_A   )

    # convert tokenizer
    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    tokenizer.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint-repo''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)